Target Drift Discriminative Network Based on Dual-template Siamese Structure in Long-term Tracking

HOU Zhiqiang; WANG Zhuo; MA Sugang; ZHAO Jiaxin; YU Wangsheng; FAN Jiulun

doi:10.11999/JEIT230496

Volume 46 Issue 4

Apr. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(4): 1458-1467

HOU Zhiqiang, WANG Zhuo, MA Sugang, ZHAO Jiaxin, YU Wangsheng, FAN Jiulun. Target Drift Discriminative Network Based on Dual-template Siamese Structure in Long-term Tracking[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1458-1467. doi: 10.11999/JEIT230496

Citation:

HOU Zhiqiang, WANG Zhuo, MA Sugang, ZHAO Jiaxin, YU Wangsheng, FAN Jiulun. Target Drift Discriminative Network Based on Dual-template Siamese Structure in Long-term Tracking[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1458-1467. doi: 10.11999/JEIT230496

Citation:

HOU Zhiqiang, WANG Zhuo, MA Sugang, ZHAO Jiaxin, YU Wangsheng, FAN Jiulun. Target Drift Discriminative Network Based on Dual-template Siamese Structure in Long-term Tracking[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1458-1467. doi: 10.11999/JEIT230496

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Target Drift Discriminative Network Based on Dual-template Siamese Structure in Long-term Tracking

doi: 10.11999/JEIT230496

1.
Institute of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
2.
Institute of Information and Navigation, Air Force Engineering University, Xi’an 710038, China

Funds: The National Natural Science Foundation of China (62072370), The Natural Science Foundation of Shaanxi Province (2023-JC-YB-598)

Received Date: 2023-05-26
Rev Recd Date: 2023-12-27

Available Online: 2024-01-02

Publish Date: 2024-04-24

Abstract

Abstract

In long-term visual tracking, most of the target loss discriminative methods require artificially determined thresholds, and the selection of optimal thresholds is usually difficult, resulting in weak generalization ability of long-term tracking algorithms. A target drift Discriminative Network (DNet) that does not require artificially selected thresholds is proposed. The network adopts Siamese structure and uses both static and dynamic templates to determine whether the tracking results are lost or not. Among them, the introduction of dynamic templates effectively improves the algorithm’s ability to adapt to changes in target appearance. In order to train the proposed target drift discriminative network, a sample-rich dataset is established. To verify the effectiveness of the proposed network, a complete long-term tracking algorithm is constructed in this paper by combining this network with the base tracker and the re-detection module. It is tested on classical visual tracking datasets such as UAV20L, LaSOT, VOT2018-LT and VOT2020-LT. The experimental results show that compared with the base tracker, the tracking accuracy and success rate are improved by 10.4% and 7.5% on UAV20L dataset, respectively.
- Long-term tracking,
- Deep learning,
- Target drift Discriminative Network(DNet),
- Siamese structure,
- Dual template

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References(22)

References

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